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DP-100 Practice Questions

Question # 1
You are building a machine learning model for translating English language textual content into French language textual content. You need to build and train the machine learning model to learn the sequence of the textual content. Which type of neural network should you use?
A. Multilayer Perceptions (MLPs)
B. Convolutional Neural Networks (CNNs)
C. Recurrent Neural Networks (RNNs)
D. Generative Adversarial Networks (GANs)


C. Recurrent Neural Networks (RNNs)

Explanation:

To translate a corpus of English text to French, we need to build a recurrent neural network (RNN).

Note: RNNs are designed to take sequences of text as inputs or return sequences of text as outputs, or both.

They’re called recurrent because the network’s hidden layers have a loop in which the output and cell state from each time step become inputs at the next time step. This recurrence serves as a form of memory. It allows contextual information to flow through the network so that relevant outputs from previous time steps can be applied to network operations at the current time step.

References:

https://towardsdatascience.com/language-translation-with-rnns-d84d43b40571

Question # 2
You use Azure Machine Learning to tram a model. You must use Baylean sampling to Tune hyperparaters. You need to select a learning_rate parameter distribution. Which two distributions can you use? Each correct answer presents a complete solution. NOTE Each correct selection is worth one point.
A. Normal
B. Uniform
C. Choice
D. LogUniform
E. QNormal


B. Uniform
C. Choice


Question # 3
You are solving a classification task. The dataset is imbalanced. You need to select an Azure Machine Learning Studio module to improve the classification accuracy. Which module should you use?
A. Fisher Linear Discriminant Analysis.
B. Filter Based Feature Selection
C. Synthetic Minority Oversampling Technique (SMOTE)
D. Permutation Feature Importance


C. Synthetic Minority Oversampling Technique (SMOTE)

Explanation:

Use the SMOTE module in Azure Machine Learning Studio (classic) to increase the number of underepresented cases in a dataset used for machine learning. SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases.

You connect the SMOTE module to a dataset that is imbalanced. There are many reasons why a dataset might be imbalanced: the category you are targeting might be very rare in the population, or the data might simply be difficult to collect. Typically, you use SMOTE when the class you want to analyze is under-represented.

[Reference:, https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote, , ]


Question # 4
You are building a binary classification model by using a supplied training set.

The training set is imbalanced between two classes.

You need to resolve the data imbalance.

What are three possible ways to achieve this goal? Each correct answer presents a complete solution NOTE: Each correct selection is worth one point.

A. Penalize the classification
B. Resample the data set using under sampling or oversampling
C. Generate synthetic samples in the minority class.
D. Use accuracy as the evaluation metric of the model.
E. Normalize the training feature set.


A. Penalize the classification
B. Resample the data set using under sampling or oversampling
D. Use accuracy as the evaluation metric of the model.

Explanation:

References:

https://machinelearningmastery.com/tactics -to-combat-imbalanced-classes-in-your-machine-learning-dataset/


Question # 5
You need to implement a model development strategy to determine a user’s tendency to respond to an ad. Which technique should you use?
A. Use a Relative Expression Split module to partition the data based on centroid distance.
B. Use a Relative Expression Split module to partition the data based on distance travelled to the event.
C. Use a Split Rows module to partition the data based on distance travelled to the event.
D. Use a Split Rows module to partition the data based on centroid distance.


A. Use a Relative Expression Split module to partition the data based on centroid distance.

Explanation:

Split Data partitions the rows of a dataset into two distinct sets.

The Relative Expression Split option in the Split Data module of Azure Machine Learning Studio is helpful when you need to divide a dataset into training and testing datasets using a numerical expression.

Relative Expression Split: Use this option whenever you want to apply a condition to a number column. The number could be a date/time field, a column containing age or dollar amounts, or even a percentage. For example, you might want to divide your data set depending on the cost of the items, group people by age ranges, or separate data by a calendar date.

Scenario:

Local market segmentation models will be applied before determining a user’s propensity to respond to an advertisement.

The distribution of features across training and production data are not consistent

References:

https://docs.microsoft.co m/en-us/azure/machine-learning/studio-module-reference/split-data


Question # 6
You arc creating a new experiment in Azure Machine Learning Studio. You have a small dataset that has missing values in many columns. The data does not require the application of predictors for each column. You plan to use the Clean Missing Data module to handle the missing data. You need to select a data cleaning method. Which method should you use?
A. Synthetic Minority
B. Replace using Probabilistic PAC
C. Replace using MICE
D. Normalization


B. Replace using Probabilistic PAC


Question # 7
You manage an Azure Machine learning workspace. The workspace includes an Azure Machine Learning kubernetes compute target configured as an Azure Kubemetes Service (AKS) cluster named AKS1 AKS1 is configured to enable the targeting of different nodes to train workloads.

You must run a command job on AK51 by using the Azure ML Python SDK v2? The command job must select different types of compute nodes. The compare node types must be specified by using a command parameter.

You need to configure the command parameter.

Which parameter should you use?

A. compute
B. environment
C. instance_type
D. limits
Explanation:

from azure.ai.ml import command

# define the command

command_job = command(

command="python -c "print('Hello world!')"",

environment="AzureML-lightgbm-3.2-ubuntu18.04-py37-cpu@latest",

compute="",

instance_type=""


Question # 8
You need to select a feature extraction method. Which method should you use?
A. Spearman correlation
B. Mutual information
C. Mann-Whitney test
D. Pearson’s correlation


A. Spearman correlation

Explanation:

Spearman's rank correlation coefficient assesses how well the relationship between two variables can be described using a monotonic function.

Note: Both Spearman's and Kendall's can be formulated as special cases of a more general correlation coefficient, and they are both appropriate in this scenario.

Scenario: The MedianValue and AvgRoomsInHouse columns both hold data in numeric format. You need to select a feature selection algorithm to analyze the relationship between the two columns in more detail.

References:

https://docs.microsoft.com/en-us/azure/machine-lear ning/studio-module-reference/feature-selection-modules


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Microsoft DP-100 Exam Dumps

Exam Name: Designing and Implementing a Data Science Solution on Azure Exam
Certification Name: Microsoft Azure

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